NannyML is strong at estimating model performance without labels and explaining drift after the fact; ValidAnytime is the complementary live alarm on the metrics you stream. The difference is anytime-validity: check as often as you like without inflating false alarms, with a shared false-alarm budget across your whole fleet.
| Capability | ValidAnytime | NannyML |
|---|---|---|
| Valid under continuous monitoring (unlimited peeking) | YesAnytime-valid by construction — Ville's inequality bounds the false-alarm rate at every look at once. | NoFixed thresholds and fixed-n tests inflate false alarms the more often you check. |
| Fleet-wide false-alarm control (online FDR) | YesA false-discovery budget shared across every stream, not per-alert luck. | NoAlerts are configured per-metric; no global bound on false discoveries. |
| Per-alarm statistical certificate | YesEvery alarm ships a guarantee tag and a theorem reference — you can audit why it fired. | NoAn alert tells you a line was crossed, not what its error guarantee is. |
| Prove it on your own history before committing (backtest gate) | YesReplay your past data: a config only ships if it stays quiet on normal history and fires on a real regression. | PartialYou can chart history, but there is no gate that validates a detector's error behaviour before it goes live. |
| Performance estimation without labels | PartialWe monitor the metrics you stream; we do not estimate unlabeled performance for you. | YesCBPE / DLE estimate model performance before labels arrive — a genuine strength. |
| Drift explanation & root-cause views | PartialWe surface the alarm and its certificate; deep feature-drift attribution is lighter. | YesRich multivariate and univariate drift decomposition. |
| Continuous monitoring without a re-run | YesStreaming and always-on; every new point updates the evidence. | PartialOriented around periodic batch analyses over chunks of data. |
We are not trying to be a dashboard, a tracer, or a platform. If you need these, reach for the right tool — often alongside ValidAnytime.
A comparison table is claims; behavior is measurable. The honest drift-detector benchmark replays every detector we ship — including the classical control-chart rules most monitoring stacks alert with — against labeled synthetic breaks, and the detector guides explain each rule, where it wins, and where it lies.
Replay your own history through the backtest gate and see whether — and at which point — ValidAnytime would have caught your regression. Free, in minutes.
Comparison based on public documentation as of July 2026; corrections welcome — email hello@validanytime.com. Source: NannyML docs